A New Method to Construct the KD Tree Based on Presorted Results

Searching is one of the most fundamental operations in many complex systems. However, the complexity of the search process would increase dramatically in high-dimensional space. K-dimensional (KD) tree, as a classical data structure, has been widely used in high-dimensional vital data search. Howeve...

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Bibliographic Details
Published inComplexity (New York, N.Y.) Vol. 2020; no. 2020; pp. 1 - 7
Main Authors Duan, Boheng, Zhao, Wenjing, Wang, Huizan, Cao, Yu, Zhang, Xiaojiang
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Wiley
Subjects
Algorithms
Arrays
Comparative analysis
Complex systems
Complexity
Construction
Data search
Data structures
Initial conditions
Methods
Search process
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Summary:Searching is one of the most fundamental operations in many complex systems. However, the complexity of the search process would increase dramatically in high-dimensional space. K-dimensional (KD) tree, as a classical data structure, has been widely used in high-dimensional vital data search. However, at present, common methods proposed for KD tree construction are either unstable or time-consuming. This paper proposed a new algorithm to construct a balanced KD tree based on presorted results. Compared with previous similar method, the new algorithm could reduce the complexity of the construction process (excluding the presorting process) from O (KNlog2N) level to O (Nlog2N) level, where K is the number of dimensions and N is the number of data. In addition, with the help of presorted results, the performance of the new method is no longer subject to the initial conditions, which expands the application scope of KD tree.
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ISSN:1076-2787
1099-0526
DOI:10.1155/2020/8883945